EPN-V2

ACIT4630 Advanced Machine Learning and Deep Learning Course description

Course name in Norwegian
Advanced Machine Learning and Deep Learning
Study programme
Master's Programme in Applied Computer and Information Technology
Weight
10.0 ECTS
Year of study
2025/2026
Programme description
Course history

Introduction

Teaching during work on the thesis is provided in the form of supervision. Working methods for writing the thesis consist mostly of self-study and research activities, as well as participation in research communities, presentations of the student's own research in research fora, research communities, and at international scientific conferences.

Recommended preliminary courses

  • Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
  • Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.

Required preliminary courses

Thesis, trial lecture, and public defence approved by expert committee.

Learning outcomes

All aids are permitted.

Content

This course covers principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in various areas such as computer vision, surveillance, assistive technology, medical imaging, etc. Therefore, the course intends to provide case studies and examples of ML and DL in solving various problems. Students can explore the tremendous potential of modern AI, ML, and DL methods and techniques in solving problems in different application domains through project work.

Teaching and learning methods

The course consists of lectures, group consultations, presentation seminars, and project work. In the seminars, students will read papers, present, and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.

Course requirements

The following required coursework must be approved before the student can take the exam:

  • Two oral presentations (one on a given topic, one on the topic of own choice)
  • Participate as a prepared opponent/discussant in two presentations from other students

There is mandatory attendance in obligatory consultation meetings and a minimum of 80% mandatory attendance in the lectures.

Students who do not meet this requirement will not be allowed to sit the exam.

Assessment

Exam in two parts:

  • A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course. Each group member receives an individual grade based on their contribution to the project.
  • Individual oral exam (about 30 minutes).

Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.

Both exams must be passed in order to pass the course.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Permitted exam materials and equipment

All aids are permitted for the project report, provided the rules for plagiarism and source referencing are complied with.

No aids are permitted for the oral exam.

Grading scale

Grade scale A-F.

Examiners

Two internal examiners. External examiner is used periodically.

Course contact person

Associate Professor Raju Shrestha